Document RAG Chatbot with Semantic Recommendations

Scenario

A developer extracts text from unstructured documents (PDFs, scanned images) using Bailian, builds a RAG knowledge base indexed in Elasticsearch, deploys a conversational chatbot over it, and layers AIRec-powered semantic recommendations on top to personalize retrieval results based on user behavior.

How the products combine

1. bailian+es · ocr-extract-and-index-for-search-f11cc4 — OCR Extract and Index for Search

See _combos/ocr-extract-and-index-for-search-f11cc4.

2. airec+opensearch+es+opensearch+oss+es+oss+opensearch+bailian+bailian+es · document-ai-rag-with-semantic-recommendations-d48dc9 — Document AI RAG with Semantic Recommendations

See _combos/document-ai-rag-with-semantic-recommendations-d48dc9.

3. bailian+es · document-extraction-to-rag-chatbot-pipeline-c495d5 — Document Extraction to RAG Chatbot Pipeline

See _combos/document-extraction-to-rag-chatbot-pipeline-c495d5.

4. bailian+es · document-extraction-to-searchable-index-pipeline-6e55f7 — Document Extraction to Searchable Index Pipeline

See _combos/document-extraction-to-searchable-index-pipeline-6e55f7.

Typical questions

• extract PDFs build RAG chatbot with recommendations
• document extraction to personalized RAG chatbot
• OCR to RAG bot with semantic recommendation layer
• 从PDF提取构建带智能推荐的RAG聊天机器人
• 文档抽取加RAG加AIRec推荐一体化部署
• build chatbot over scanned docs with personalization
• end to end document RAG with AIRec recommendations
• unstructured docs to RAG chatbot plus recommendation engine

Q: How can I build a RAG chatbot with semantic recommendations from extracted documents? A: You can build this system by combining Bailian for document extraction, Elasticsearch for indexing the RAG knowledge base, and AIRec for semantic recommendations. The workflow extracts text from unstructured files like PDFs and scanned images using Bailian, indexes the content in Elasticsearch, and layers AIRec on top to personalize retrieval results based on user behavior.